We've updated our Privacy Policy to make it clearer how we use your personal data.
We use cookies to provide you with a better experience, read our Cookie Policy

EP38450
Abstract: Fast and accurate identification and characterization of drug metabolites play a critical role in preclinical and clinical development stages to assist lead compound structure optimization, screening drug candidates, and finding active or potentially toxic metabolites. In this work, a DDA PASEF non targeted LC timsTOF Pro metabolomics workflow was conducted to profile and characterize drug metabolites. Metabolites were postulated by utilizing BioTransformer[1], a knowledge and machine learning based approach to predict small molecules metabolism. Metabolite structures were elucidated by in silico fragmentation, MS/MS spectral library and comparison of acquired to reference or predicted CCS values using a novel CCS prediction algorithm. Together, each of these steps forms a fully integrated workflow that utilizes the four dimensional data to ensure low level drug metabolites can be annotated.Summary: In this work, a DDA PASEF non targeted LC timsTOF Pro metabolomics workflow was conducted to profile and characterize drug metabolites.References: (1) Djoumbou Feunang et al .; Journal of Cheminform, 2019:11:2
(2) Rouini M. et al .; DARU Journal of Pharmaceutical Sciences, 2013, 21:17.
(2) Rouini M. et al .; DARU Journal of Pharmaceutical Sciences, 2013, 21:17.
Ask the author a question about this poster.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Related Posters
The QSP model of microglia role in tau pathology and neurodegeneration
Stepan Lerner, Maria Myshkina, Tatiana Karelina
DNA-Stable Isotope Probing Technology (DNA-SIP)
Alex Brown
Suppository Bases
Helen
Physico-Chemical Characterization of Lyophilizates
Helen
Estimation of Imax and IC50 for PD1-PDL1 inhibition of T cell proliferation
Oleg Demin